Ambulatory Monitoring of Heart Sounds via an Implanted Device Is Superior to Auscultation for Prediction of Heart Failure Events

Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm

Background

Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable cardioverter-defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation.

Objective

This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm.

Methods

The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th-75th percentile: 15-37).

Results

During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02-9.72; P = .045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74-9.06; P=.001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74%-98%]) and 55% specificity (95% CI [51%-60%]), while BiV% ≤96% predicted alerts with 74% sensitivity (95% CI [55%-88%]) and 81% specificity (95% CI [77%-85%]).

Conclusion

A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.

Beat-to-beat alterations of acoustic intensity and frequency at the maximum power of heart sounds are associated with NT-proBNP levels

Background

Auscultatory features of heart sounds (HS) in patients with heart failure (HF) have been studied intensively. Recent developments in digital and electrical devices for auscultation provided easy listening chances to recognize peculiar sounds related to diastolic HS such as S3 or S4. This study aimed to quantitatively assess HS by acoustic measures of intensity (dB) and audio frequency (Hz).

Methods

Forty consecutive patients aged between 46 and 87 years (mean age, 74 years) with chronic cardiovascular disease (CVD) were enrolled in the present study after providing written informed consent during their visits to the Kitasato University Outpatient Clinic. HS were recorded at the fourth intercostal space along the left sternal border using a highly sensitive digital device. Two consecutive heartbeats were quantified on sound intensity (dB) and audio frequency (Hz) at the peak power of each spectrogram of S1-S4 using audio editing and recording application software. The participants were classified into three groups, namely, the absence of HF (n = 27), HF (n = 8), and high-risk HF (n = 5), based on the levels of NT-proBNP < 300, ≥300, and ≥900 pg/ml, respectively, and also the levels of ejection fraction (EF), such as preserved EF (n = 22), mildly reduced EF (n = 12), and reduced EF (n = 6).

Results

The intensities of four components of HS (S1-S4) decreased linearly (p < 0.02-0.001) with levels of body mass index (BMI) (range, 16.2-33.0 kg/m2). Differences in S1 intensity (ΔS1) and its frequency (ΔfS1) between two consecutive beats were non-audible level and were larger in patients with HF than those in patients without HF (ΔS1, r = 0.356, p = 0.024; ΔfS1, r = 0.356, p = 0.024). The cutoff values of ΔS1 and ΔfS1 for discriminating the presence of high-risk HF were 4.0 dB and 5.0 Hz, respectively.

Conclusions

Despite significant attenuations of all four components of HS by BMI, beat-to-beat alterations of both intensity and frequency of S1 were associated with the severity of HF. Acoustic quantification of HS enabled analyses of sounds below the audible level, suggesting that sound analysis might provide an early sign of HF.

The Potential of the HeartLogicTM Algorithm in Patients with a Left Ventricular Assist Device, an Initial Report

BACKGROUND

Survival and quality-of-life of left ventricular assist device (LVAD) recipients improved significantly because of growing experience and technological advances. However, LVAD-related complication rates, including recurrent episodes of congestion, remain high. Early detection of fluid retention to provide a time-window for medical intervention is the pillar in preventing hospitalizations. The multisensory HeartLogicTM algorithm accurately detected impending congestion in ambulant heart failure patients. The aim of the current study is to investigate the feasibility of HeartLogicTM-driven care in LVAD patients.

METHODS

Consecutive LVAD destination therapy patients were followed-up according the structured HeartLogicTM-based heart failure carepath. An alert triggered a device check-up, and the heart failure team contacted the patient to evaluate for signs and symptoms of impending congestion. An alert was adjudicated as true positive or unexplained. An episode of congestion not preceded by an alert was deemed as a false negative.

RESULTS

Data from 7 patients were included: the median age was 67 years [IQR 61-71], 71% were male and 71% had a non-ischemic aetiology. Total follow-up entailed 12 patient-years. All patients experienced at least one alert. In total, 33 alerts were observed. Majority of alerts (70%, n = 23) were driven by congestion and one alerts (15%) were clinically meaningful but not primarily fluid-retention-related (e.g., altered hemodynamic triggered by a pump thrombosis). Of all the alerts, five (15%) were classified as an unexplained alert, and during follow-up, four false negative episodes were documented.

CONCLUSIONS

HeartLogicTM-driven care with continuous monitoring to detect impending fluid retention in LVAD patients was feasible and deserves further prospective validation.

Performance of a multi-sensor implantable defibrillator algorithm for heart failure monitoring in the presence of atrial fibrillation

AIMS

The HeartLogic Index combines data from multiple implantable cardioverter defibrillators (ICDs) sensors and has been shown to accurately stratify patients at risk of heart failure (HF) events. We evaluated and compared the performance of this algorithm during sinus rhythm and during long-lasting atrial fibrillation (AF).

METHODS AND RESULTS

HeartLogic was activated in 568 ICD patients from 26 centres. We found periods of ≥30 consecutive days with an atrial high-rate episode (AHRE) burden <1 h/day and periods with an AHRE burden ≥20 h/day. We then identified patients who met both criteria during the follow-up (AHRE group, n = 53), to allow pairwise comparison of periods. For control purposes, we identified patients with an AHRE burden <1 h throughout their follow-up and implemented 2:1 propensity score matching vs. the AHRE group (matched non-AHRE group, n = 106). In the AHRE group, the rate of alerts was 1.2 [95% confidence interval (CI): 1.0-1.5]/patient-year during periods with an AHRE burden <1 h/day and 2.0 (95% CI: 1.5-2.6)/patient-year during periods with an AHRE-burden ≥20 h/day (P = 0.004). The rate of HF hospitalizations was 0.34 (95% CI: 0.15-0.69)/patient-year during IN-alert periods and 0.06 (95% CI: 0.02-0.14)/patient-year during OUT-of-alert periods (P < 0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95% CI: 1.67-55.31) during periods with an AHRE burden <1 h/day and 2.70 (95% CI: 1.01-28.33) during periods with an AHRE burden ≥20 h/day. In the matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95% CI: 0.12-0.60)/patient-year during IN-alert periods and 0.04 (95% CI: 0.02-0.08)/patient-year during OUT-of-alert periods (P < 0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44).

CONCLUSION

Patients received more alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio in comparison with non-AF periods and non-AF patients.

CLINICAL TRIAL REGISTRATION

http://clinicaltrials.gov/Identifier: NCT02275637.

Predicting all-cause mortality by means of a multisensor implantable defibrillator algorithm for heart failure monitoring

BACKGROUND

The HeartLogic algorithm (Boston Scientific) has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation.

OBJECTIVE

The purpose of this study was to determine whether remotely monitored data from this algorithm could be used to identify patients at high risk for mortality.

METHODS

The algorithm combines implantable cardioverter-defibrillator (ICD)-measured accelerometer-based heart sounds, intrathoracic impedance, respiration rate, ratio of respiration rate to tidal volume, night heart rate, and patient activity into a single index. An alert is issued when the index crosses a programmable threshold. The feature was activated in 568 ICD patients from 26 centers.

RESULTS

During median follow-up of 26 months [25th-75th percentile 16-37], 1200 alerts were recorded in 370 patients (65%). Overall, the time IN-alert state was 13% of the total observation period (151/1159 years) and 20% of the follow-up period of the 370 patients with alerts. During follow-up, 55 patients died (46 in the group with alerts). The rate of death was 0.25 per patient-year (95% confidence interval [CI] 0.17-0.34) IN-alert state and 0.02 per patient-year (95% CI 0.01-0.03) OUT of the alert state, with an incidence rate ratio of 13.72 (95% CI 7.62-25.60; P <.001). After multivariate correction for baseline confounders (age, ischemic cardiomyopathy, kidney disease, atrial fibrillation), the IN-alert state remained significantly associated with the occurrence of death (hazard ratio 9.18; 95% CI 5.27-15.99; P <.001).

CONCLUSION

The HeartLogic algorithm provides an index that can be used to identify patients at higher risk for all-cause mortality. The index state identifies periods of significantly increased risk of death.

Changes in cardiac acoustic biomarkers before and after cardiac events in a patient with right-sided heart failure due to cor pulmonale

Chronic heart failure (HF) has various phenotypes. It is accompanied by repeated hospitalizations over a long period. Therefore, accumulating long-term observational data of patients with various backgrounds is important to establish a prediction technology for the exacerbation of HF. In a patient with chronic right-sided HF caused by cor pulmonale, heart sounds and electrocardiograms were recorded at home or our hospital twice a week for 7 months including the stable (31 days), pre-exacerbation (2 weeks just before the onset of exacerbation), and hospitalization periods and quantified as cardiac acoustic biomarkers (CABs) using AUDICOR technology (Inovise Medical, Inc., Portland, OR, USA). The relationship between the change in CABs and hospitalization events due to HF were investigated. During the pre-exacerbation period just before the onset of exacerbation of HF leading to hospitalization, inaudible changes in the third heart sound (S3) strength that were probably derived from the right heart were observed. Although the values of the fourth heart sound (S4) strength were high during the stable and pre-exacerbation period, values decreased markedly during hospitalization. These findings suggest that CABs including S3 and S4 are useful for the early detection of signs of HF exacerbation.

Learning Objective

In a case of chronic right-sided heart failure, the change in the third heart sound (S3) caused by the right ventricle could be detected using cardiac acoustic biomarkers in exacerbations of heart failure. Even if S3 is inaudible by auscultation, it is possible to observe its changes using quantification technology.

Temporal Association of Atrial Fibrillation With Cardiac Implanted Electronic Device Detected Heart Failure Status

OBJECTIVES

This study sought to investigate the temporal association between changes in physiologic heart failure (HF) sensors, atrial fibrillation (AF) progression, and clinical HF in patients with cardiac resynchronization therapy implantable defibrillators (CRT-D) designed to monitor AF and HF daily.

BACKGROUND

AF is a common comorbidity in HF; however, it is unclear if HF triggers AF, or vice-versa. Current implantable cardiac devices have sensors capable of quantifying HF status, which permits a greater understanding of the impact of AF on HF status and may help guide treatment.

METHODS

The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) study collected multiple sensor data indicative of HF status in patients with CRT-D followed for up to 12 months. Patients were grouped according to their longest daily AF burden: 1) at least 24 hours of AF (HIGH AF); 2) between 6 minutes and 24 hours (MID AF); and 3) <6 minutes (NO AF). Sensor data were aligned to the first qualifying AF event or a randomly selected day for patients in the NO AF group.

RESULTS

Among 869 patients with daily AF data available, 98 patients had HIGH AF, 141 patients MID AF, and 630 patients NO AF. At baseline, history of AF, N-terminal pro hormone B-type natriuretic peptide and device-measured S3 were associated with development of AF. HeartLogic index increased before AF onset (Δ HeartLogic = 9.83 ± 2.49; P < 0.001). Multivariable time-dependent Cox regression showed an increased risk for HF events following a 24-hour AF episode compared with no 24-hour AF (hazard ratio: 1.96; 95% confidence interval: 1.03-3.74).

CONCLUSIONS

Device-measured HF indicators worsened before AF onset, whereas clinical HF deterioration only became apparent after AF occurred. Thus, the sensitivity of methods to ascertain AF and HF status appear to influence the direction of perceived causality. (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients [MultiSENSE]; NCT01128166).

Implantable Cardioverter Defibrillator Multisensor Monitoring during Home Confinement Caused by the COVID-19 Pandemic

Simple Summary

During the COVID-19 pandemic, utilization of remote monitoring platforms was recommended. The HeartLogic algorithm identifies patients at risk of heart failure events, combining multiple sensors available on implantable cardioverter defibrillators. This analysis examined how multiple CIED sensors behave in periods of anticipated restrictions pertaining to physical activity. We demonstrated a significant drop in median activity level immediately after the implementation of stay-at-home orders, whereas there was no difference in the other contributing sensors. The weekly rate of heart failure alerts was significantly higher during the lockdown and post-lockdown than that reported in the pre-lockdown.

Abstract

Aims: The utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic index combines multiple implantable cardioverter defibrillator (ICD) sensors and has proved to be a predictor of impending heart failure (HF) decompensation. We examined how multiple ICD sensors behave in the periods of anticipated restrictions pertaining to physical activity. Methods: The HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from 1 January to 19 July 2020, was divided into three phases: pre-lockdown (weeks 1–11), lockdown (weeks 12–20), post-lockdown (weeks 21–29). Results: Immediately after the implementation of stay-at-home orders (week 12), we observed a significant drop in median activity level whereas there was no difference in the other contributing parameters. The median composite HeartLogic index increased at the end of the Lockdown. The weekly rate of alerts was significantly higher during the lockdown (1.56 alerts/week/100 pts, 95%CI: 1.15–2.06; IRR = 1.71, p = 0.014) and post-lockdown (1.37 alerts/week/100 pts, 95%CI: 0.99–1.84; IRR = 1.50, p = 0.072) than that reported in pre-lockdown (0.91 alerts/week/100 pts, 95%CI: 0.64–1.27). However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers and the proportion of alerts fully managed remotely. Conclusions: During the lockdown, the system detected a significant drop in the median activity level and generated a higher rate of alerts suggestive of worsening of the HF status.

Analysis of Cardiac Vibration Signals Acquired From a Novel Implant Placed on the Gastric Fundus

The analysis of cardiac vibration signals has been shown as an interesting tool for the follow-up of chronic pathologies involving the cardiovascular system, such as heart failure (HF). However, methods to obtain high-quality, real-world and longitudinal data, that do not require the involvement of the patient to correctly and regularly acquire these signals, remain to be developed. Implantable systems may be a solution to this observability challenge. In this paper, we evaluate the feasibility of acquiring useful electrocardiographic (ECG) and accelerometry (ACC) data from an innovative implant located in the gastric fundus. In a first phase, we compare data acquired from the gastric fundus with gold standard data acquired from surface sensors on 2 pigs. A second phase investigates the feasibility of deriving useful hemodynamic markers from these gastric signals using data from 4 healthy pigs and 3 pigs with induced HF with longitudinal recordings. The following data processing chain was applied to the recordings: (1) ECG and ACC data denoising, (2) noise-robust real-time QRS detection from ECG signals and cardiac cycle segmentation, (3) Correlation analysis of the cardiac cycles and computation of coherent mean from aligned ECG and ACC, (4) cardiac vibration components segmentation (S1 and S2) from the coherent mean ACC data, and (5) estimation of signal context and a signal-to-noise ratio (SNR) on both signals. Results show a high correlation between the markers acquired from the gastric and thoracic sites, as well as pre-clinical evidence on the feasibility of chronic cardiovascular monitoring from an implantable cardiac device located at the gastric fundus, the main challenge remains on the optimization of the signal-to-noise ratio, in particular for the handling of some sources of noise that are specific to the gastric acquisition site.

Multiparametric Implantable Cardioverter-Defibrillator Algorithm for Heart Failure Risk Stratification and Management: An Analysis in Clinical Practice

BACKGROUND

The HeartLogic algorithm combines multiple implantable cardioverter-defibrillator sensors to identify patients at risk of heart failure (HF) events. We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events.

METHODS

The HeartLogic feature was activated in 366 implantable cardioverter-defibrillator and cardiac resynchronization therapy implantable cardioverter-defibrillator patients at 22 centers. The median follow-up was 11 months [25th-75th percentile: 6-16]. The HeartLogic algorithm calculates a daily HF index and identifies periods IN alert state on the basis of a configurable threshold.

RESULTS

The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients. The time IN alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations, and 8 patients died of HF during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (hazard ratio, 24.53 [95% CI, 8.55-70.38], P<0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with less HF events (hazard ratio, 0.37 [95% CI, 0.14-0.99], P=0.047). No differences in event rates were observed between in-office and remote alert management.

CONCLUSIONS

This multiparametric algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required to effectively manage HeartLogic alerts.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02275637.

Multiparameter diagnostic sensor measurements in heart failure patients presenting with SARS-CoV-2 infection

Aims

Implantable device‐based sensor measurements including heart sounds, markers of ventilation, and thoracic impedance have been shown to predict heart failure (HF) hospitalizations. We sought to assess how these parameters changed prior to COVID‐19 (Cov‐19) and how these compared with those presenting with decompensated HF or pneumonia.

Methods and results

This retrospective analysis explores patterns of changes in daily measurements by implantable sensors in 10 patients with Cov‐19 and compares these findings with those observed prior to HF (n = 88) and pneumonia (n = 12) hospitalizations from the MultiSENSE, PREEMPT‐HF, and MANAGE‐HF trials. The earliest sensor changes prior to Cov‐19 were observed in respiratory rate (6 days) and temperature (5 days). There was a three‐fold to four‐fold greater increase in respiratory rate, rapid shallow breathing index, and night heart rate compared with those presenting with HF or pneumonia. Furthermore, activity levels fell more in those presenting with Cov‐19, a change that was often sustained for some time. In contrast, there were no significant changes in 1st or 3rd heart sound (S₁ and S₃) amplitude in those presenting with Cov‐19 or pneumonia compared with the known changes that occur in HF decompensation.

Conclusions

Multi‐sensor device diagnostics may provide early detection of Cov‐19, distinguishable from worsening HF by an extreme and fast rise in respiratory rate along with no changes in S3.

Temporal changes of cardiac acoustic biomarkers and cardiac function in acute decompensated heart failure

AIMS

Relationships between cardiac acoustic biomarkers (CABs) measured by acoustic cardiography and clinical outcomes have been reported in heart failure (HF) patients. However, no studies have investigated the temporal change of CABs and the corresponding changes in HF status. The purpose of this study was to assess whether the temporal changes of CABs in patients with acute decompensated heart failure (ADHF) reflect changes in cardiac function and status.

METHODS AND RESULTS

Sixty ADHF patients were enrolled prospectively. CABs and echocardiography data were collected at admission, before discharge, and at the first clinic visit. CABs included electromechanical activation time (EMAT); the time interval from Q wave onset on electrocardiography to the first heart sound (S1), QoS2; the time interval from Q wave onset on electrocardiography to the second heart sound (S2); and third heart sound (S3) and fourth heart sound (S4) intensities, defined as the peak-to-peak amplitudes of S3 and S4. EMATc (EMAT/RR) (P = 0.001), S3 intensity (P < 0.001), and S4 intensity (P < 0.001) were significantly decreased, and QoS2 (P = 0.005) was significantly increased from admission to discharge. The change in S3 intensity was significantly correlated with that of E/A (ρ = 0.571, P < 0.001), and the extended QoS2 was also significantly correlated with the increase in the stroke volume index (ρ = 0.383, P = 0.004).

CONCLUSIONS

Some CABs in ADHF patients changed significantly in the normal direction throughout the treatment course and could be useful biomarkers in ADHF management.

Small decreases in biventricular pacing percentages are associated with multiple metrics of worsening heart failure as measured from a cardiac resynchronization therapy defibrillator

BACKGROUND

Lower BiVentricular (BiV) pacing percentages have been associated with significantly worse survival in patients with chronic heart failure (HF). However, the pathophysiology behind this observation has not been further delineated. This analysis evaluated whether small incremental decreases in BiV pacing percentages were associated with worse measures, related to HF physiology using individual sensor trends and the HeartLogic composite index.

METHODS

Sensor data was obtained from 900 ambulatory HF patients with implanted CRT devices. The percent of cardiac cycles with BiV pacing was assessed for periods (median = 7.3 days) between data downloads (median = 55 periods/patient).

RESULTS

The third heart sound (S3), respiration rate, RSBI, and night-time heart rate were significantly elevated with sub-optimal pacing (<98%), while the first heart sound (S1), thoracic impedance, and activity were significantly lower. All sensor changes were in the direction associated with worsening HF. While IN the HeartLogic alert state (threshold above an Index of 16) the odds of optimal BiV pacing (≥98%) were less than when OUT of the HeartLogic alert state for a given subject (OR: 0.655; 95% CI: 0.626-0.686; p < 0.0001). The percent BiV pacing was reduced and the HeartLogic Index was increased in the periods surrounding HFhospitalizations.

CONCLUSION

Lower BiV pacing percent is associated with multiple sensor changes indicative of worsening HF, and patients in HeartLogic alert are more likely to have suboptimal BiV pacing. Collectively, these data provide strong evidence that even small decreases in BiV percent pacing can lead to worsening HF.

Real-world outcomes in cardiac resynchronization therapy patients: design and baseline demographics of the SMART- Registry

AIMS

The SMART (Strategic MAnagement to optimize response to cardiac Resynchronization Therapy) Registry was designed to assess real-world outcomes for patients receiving a cardiac resynchronization therapy defibrillator (CRT-D) and to better understand which programming and optimization techniques are used and how effective they are.

METHODS AND RESULTS

The SMART Registry is a global, multicentre, prospective, observational, post-market CRT-D registry with a planned enrolment of 2000 subjects from a maximum of 200 sites in Europe, North America, and Asia-Pacific region. Each subject will be followed up for a minimum of 12 months. The primary endpoint of CRT response rate at 12 months is defined by a clinical composite score of all-cause mortality, heart failure events, New York Heart Association Class, and quality of life as assessed by a patient global assessment instrument. A subgroup composed of the first 103 consecutive European subjects implanted with an NG4 device will have left ventricular multisite pacing feature enabled at any time during the initial 12 months of follow-up. The primary endpoint for this sub-analysis will be the NG4 PG-related complication-free rate at 36 months.

CONCLUSIONS

The SMART Registry achieved its recruitment target in August 2019, with 2014 patients enrolled. The baseline demographics demonstrated that patients were generally older, with greater co-morbidity, and on more contemporary medical therapy than in the key CRT trials. The results of the SMART Registry will determine which programming and optimization techniques are effective in this real-world population.

Cardiac Rehab in the COVID Era and Beyond: mHealth and Other Novel Opportunities

Purpose of Review

The COVID-19 pandemic has forced many center-based cardiac rehabilitation (CBCR) programs to close or limit their usual offerings. In order for patients to continue to benefit from CR, programs need to rapidly adapt to the current environment. This review highlights ways CR has evolved, and reviews the history of CR and recent advancements in telemedicine including remote patient monitoring, and mobile health that can be applied to CR.

Recent Findings

Despite that initial studies indicate that home-based CR (HBCR) is safe and effective, HBCR has faced several challenges that have prevented it from becoming more widely implemented. Many previous concerns can now be addressed through the use of new innovations in home-based healthcare delivery.

Summary

Since its inception, CR has become increasingly recognized as an important tool to improve patient mortality and quality of life in a broad range of cardiac diseases. While there has been little need to modify the delivery of CR since the 1950s, COVID-19 now serves as the necessary impetus to make HBCR an equal alternative to CBCR.

Use of a novel implantable cardioverter-defibrillator multisensor algorithm for heart failure monitoring in a COVID-19 patient: A case report

We report the case of a patient implanted with an implantable defibrillator endowed with a multisensor algorithm for heart failure monitoring. Automatic measurement of multiple clinical variables allowed to detect impending heart failure decompensation and showed its ability to facilitate differential diagnosis in the context of the current COVID-19 pandemic.

Multiparameter diagnostic sensor measurements during clinically stable periods and worsening heart failure in ambulatory patients

Aims

This study aims to characterize the range of implantable device‐based sensor values including heart sounds, markers of ventilation, thoracic impedance, activity, and heart rate for patients with heart failure (HF) when patients were deemed to be in clinically stable periods against the time course of acute decompensation and recovery from HF events.

Methods and results

The MultiSENSE trial followed 900 patients implanted with a COGNIS CRT‐D for up to 1 year. Chronic, ambulatory diagnostic sensor data were collected and evaluated during clinically stable periods (CSP: unchanged NYHA classification, no adverse events, and weight change ≤2.27 kg), and in the timeframe leading up to and following HF events (HF admissions or unscheduled visits with intravenous HF treatment). Physiologic sensor data from 1667 CSPs occurring in 676 patients were compared with those data leading up to and following 192 HF events in 106 patients. Overall, the mean age was 66.6 years, and the population were predominantly male (73%). Patients were primarily in NYHA II (67%), with a mean LVEF of 29.6% and median NT‐proBNP of 754.5 pg/mL. Sensor values during CSP were poorer in patients who had HF events during the study period than those without HF events, including first heart sound (S1: 2.18 ± 0.84 mG vs. 2.62 ± 0.95 mG, P = 0.002), third heart sound (S3: 1.13 ± 0.36 mG vs. 0.91 ± 0.30 mG, P < 0.001), thoracic impedance (45.66 ± 8.78 Ohm vs. 50.33 ± 8.43 Ohm, P < 0.001), respiratory rate (19.09 ± 3.10 br/min vs. 17.66 ± 2.39 br/min, P = 0.002), night time heart rate (73.39 ± 8.36 b.p.m. vs. 69.56 ± 8.09 b.p.m., P = 0.001), patient activity (1.69 ± 1.84 h vs. 2.56 ± 2.20 h, P = 0.006), and HeartLogic index (11.07 ± 12.14 vs. 5.31 ± 5.13, P = 0.001). Sensor parameters measured worsening status leading up to HF events with recovery of values following treatment.

Conclusions

Device‐based physiologic sensors not only revealed progressive worsening leading up to HF events but also differentiated patients at increased risk of HF events when presumed to be clinically stable.

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

Advances in cardiovascular monitoring technologies have resulted in an influx of consumer-targeted wearable sensors that have the potential to detect numerous heart conditions. In this Review, Krittanawong and colleagues describe processes involved in biosignal acquisition and analysis of cardiovascular monitors, as well as their associated ethical, regulatory and legal challenges.

Advances in the use of cardiovascular monitoring technologies, such as the development of novel portable sensors and machine learning algorithms that can provide near-real-time diagnosis, have the potential to provide personalized care.

Wearable sensor technologies can detect numerous biosignals, such as cardiac output, blood-pressure levels and heart rhythm, and can integrate multiple modalities.

The use of novel biosignals for diagnosis raises concerns regarding accuracy and actionability within clinical guidelines, in addition to medical, legal and ethical issues.

Machine learning-based interpretation of biosensor data can facilitate rapid evaluation of the haemodynamic consequences of heart failure or arrhythmias, but is limited by the presence of noise and training data that might not be representative of the real-world clinical setting.

The use of data derived from cardiovascular monitoring devices is associated with numerous challenges, such as data security, accessibility and ownership, in addition to other ethical and regulatory concerns.